Rapid prototyping and in vitro modeling of patient-specific coronary artery bypass grafts

ABSTRACT

The present disclosure describes a system and a method for producing patient-specific small diameter vascular grafts (SDVG) for coronary artery bypass graft (CABG) surgery. In some embodiments, the method for producing SDVGs includes non-invasive quantification of patient-specific coronary and vascular physiology by applying computational fluid dynamics (CFD), rapid prototyping, and in vitro techniques to medical images and coupling the quantified patient-specific coronary and vascular physiology from the CFD to computational fluid-structure interactions and SDVG structural factors to design a patient-specific SDVG.

RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 62/365,474, filed on Jul. 22, 2016 and titled COMPUTATIONAL MODELINGAND RAPID PROTOTYPING OF PATIENT-SPECIFIC CORONARY ARTERY BYPASS GRAFTS,which is hereby incorporated by reference in its entirety.

BACKGROUND

The following description is provided to assist the understanding of thereader. None of the information provided or references cited is admittedto be prior art to the present technology.

Coronary artery disease (CAD) is a large cause of patient morbidity andmortality. In the United States, CAD affects more than 16 millionadults, accounts for more than ⅓ of deaths, and is responsible for morethan 1.2 million hospitalizations annually. Despite medical therapy,coronary revascularization is required for more than 1.5 millionindividuals annually. For stable individuals with complex multivesseldisease, a coronary artery bypass graft (CABG) remains the mainstay oftreatment for myocardial ischemia reduction and is performed for nearly400,000 individuals in the U.S. annually.

Post-CABG morbidity and mortality remain high. Early CABGocclusion—particularly of saphenous venous grafts (SVG)—occurs in 10% ofgrafts, with 50% failing by 18 months. Further, 30% of individuals donot even possess the suitable autologous conditions for CABG. Autologousgrafts can be produced in a variety of sizes. In general, small diametergrafts can include grafts having diameters of less than about 6 mm.Medium diameter grafts can include grafts having diameters between about6 mm and about 8 mm. Large diameter grafts can include grafts havingdiameters of greater than about 8 mm.

Common to the failure of small diameter vascular grafts (SDVG) has beenan array of overlapping factors, with inadequate consideration of thetotality of variables that influence long-term SDVG patency. These caninclude: (i) patient-specific cardiothoracic anatomy and physiology toinform graft size, location, angle and path for optimized flow; (ii)SDVG features such as mechanical properties that ensure adequate graftcompliance to accommodate pulsatile flow states while avoiding kinkingand diminished durability, and surface biocompatibility to minimizeinflammation and platelet adhesion; and (iii) surgical-related factors,such as optimal revascularization strategies to properly locate graftsto specific arteries to relieve ischemia, as well as to minimizeanastomotic occlusion or flow reduction.

SUMMARY

According to one aspect of the disclosure, a method includes receivingat least one medical image of a subject. The method also includesdetermining a coronary artery volumetric geometry of the subject. Thevolumetric geometry is determined responsive to the at least one medicalimage. The method further includes generating a patient-specificvascular graph model responsive to the determined coronary arteryvolumetric geometry. The method further includes determining, using thepatient-specific vascular graph model, a hemodynamic profile. The methodfurther includes producing a patient-specific vascular graft based onthe hemodynamic profile and the determined coronary artery volumetricgeometry.

In some implementations, the method can include printing thepatient-specific vascular graft with a 3D printer. The patient-specificvascular graft can have an internal diameter between about 0.5 mm andabout 6 mm. In some implementations, the patient-specific vascular graftis formed from a biocompatible polymer. In some implementations, thepatient-specific vascular graft exhibits continuously spatially varyingmechanical properties along at least a portion of its length.

In some implementations, determining the coronary artery volumetricgeometry can include determining an artery centerline of the coronaryartery. The method can also include performing lumen segmentation of thecoronary artery volumetric geometry. In some implementations, the methodcan include calculating at least one of a flow, a velocity, a pressure,or a shear stress of the patient-specific vascular graft.

In some implementations, calculating at least one of the flow, thevelocity, the pressure, or the shear stress of the patient-specificvascular graft can include providing a flow system comprising a pump,the patient-specific vascular graft, and at least one measurement tool.The at least one measurement tool can be at least one of a pressuregauge, a flow gauge, a velocity gauge, a particle image velocimetrydevice, a pressure guide wire, a flow guide wire, an optical coherencetomography device, or a strain sensor. In some implementations,generating the hemodynamic profile can further include applying anoptimization technique based on a set of parameters including at leastone of a flow rate, a pressure gradient, a shear stress, or a flowoscillation. In some implementations, the at least one medical imageincludes at least one computed tomographic angiography (CTA) image.

According to another aspect of the disclosure, a system includes atleast one processor and a memory unit that stores processor executableinstructions. When the at least one processor executes the processorexecutable instructions, the at least one processor receives at leastone medical image of a subject. The processor also determines a coronaryartery volumetric geometry of the subject responsive to the at least onemedical image. The processor generates a patient-specific vascular graphmodel responsive to the determined coronary artery volumetric geometry.The processor also determines, using the patient-specific vascular graphmodel, a hemodynamic profile. The processor also generates instructionsfor producing a patient-specific vascular graft based on the hemodynamicprofile and the determined coronary artery volumetric geometry.

In some implementations, execution of the processor executableinstructions causes the at least one processor to transmit theinstructions to a 3D printer. In some implementations, the instructionsfor producing the patient-specific vascular graft indicate that thepatient-specific vascular graft should be formed from a biocompatiblepolymer. In some implementations, the instructions for producing thepatient-specific vascular graft indicate that the patient-specificvascular graft should exhibit continuously spatially varying mechanicalproperties along at least a portion of its length.

In some implementations, execution of the processor executableinstructions further causes the at least one processor to determine thecoronary artery volumetric geometry by determining an artery centerlineof the subject. In some implementations, execution of the processorexecutable instructions further causes the at least one processor toperform a lumen segmentation of the coronary artery volumetric geometry.

In some implementations, execution of the processor executableinstructions further causes the at least one processor to calculate atleast one of a flow, a velocity, a pressure, or a shear stress of thepatient-specific vascular graft.

In some implementations, execution of the processor executableinstructions further causes the processor to calculate at least one ofthe flow, the velocity, the pressure, or the shear stress of thepatient-specific vascular graft based on an output received from atleast one measurement tool included within a flow system including apump and the patient-specific vascular graft. In some implementations,the at least one measurement tool includes at least one of a pressuregauge, a flow gauge, a velocity gauge, a particle image velocimetrydevice, a pressure guide wire, a flow guide wire, an optical coherencetomography device, or a strain sensor. In some implementations, the atleast one medical image includes at least one (CTA) image.

BRIEF DESCRIPTION OF DRAWINGS

The figures, described herein, are for illustration purposes only. It isto be understood that in some instances various aspects of the describedimplementations may be shown exaggerated or enlarged to facilitate anunderstanding of the described implementations. In the drawings, likereference characters generally refer to like features, functionallysimilar and/or structurally similar elements throughout the variousdrawings. The drawings are not necessarily to scale, emphasis insteadbeing placed upon illustrating the principles of the teachings. Thedrawings are not intended to limit the scope of the present teachings inany way. The system and method may be better understood from thefollowing illustrative description with reference to the followingdrawings in which:

FIG. 1 illustrates an example system for producing a patient-specificvascular graft.

FIG. 2 illustrates a 3D image-based modeling of coronary arteries andmyocardium.

FIG. 3 illustrates an example calculation of coronary pressure andvelocity.

FIG. 4 illustrates the differences in velocities (left) and wall shearstress (right) with respect to angle of anastomoses.

FIG. 5A illustrates a patient-specific vascular graft as a 3D printedphysical model.

FIG. 5B illustrates an example in vitro flow circulation system.

FIGS. 5C and 5D illustrate the tracking of particle movements andcomputing of pulsatile fluid velocities within small-diameter tubularmodels.

FIG. 6 illustrates an in vitro benchtop flow system generating realisticpressure using downstream resistance based upon physiologicallyprescribed flow.

FIG. 7 illustrates example mechanical properties of fixed aorta tissue.

FIG. 8 illustrates an example flow diagram of an experiment.

FIG. 9 illustrates an example experimental timeline.

FIG. 10 illustrates an example method for producing a patient-specificvascular graft using the system illustrated in FIG. 1.

FIG. 11 illustrates an example flow system with a patient-specificvascular graft.

DETAILED DESCRIPTION

The various concepts introduced above and discussed in greater detailbelow may be implemented in any of numerous ways, as the describedconcepts are not limited to any particular manner of implementation.Examples of specific implementations and applications are providedprimarily for illustrative purposes.

In general, the present disclosure describes a system and a method forproducing and validating patient-specific small diameter vascular grafts(SDVG), and models thereof, for coronary artery bypass graft (CABG)surgery. The system can generate patient-specific mathematical andphysical models of SDVGs that reflect realistic physiologic conditions,which can enable individual and integrated assessment of features thatencourage graft patency, and subsequent use of the optimized model forin vivo implantation. In some implementations, the SDVG has an internaldiameter between about 0.5 mm and about 6 mm. In some embodiments, themethod for producing SDVGs includes non-invasive quantification ofpatient-specific coronary and vascular physiology by applyingcomputational fluid dynamics (CFD) to one or more medical images andcoupling the quantified patient-specific coronary and vascularphysiology from the CFD to computational fluid-structure interactionsand SDVG structural factors to design a patient-specific SDVG. In someimplementations, the medical images can be generated using magneticresonance imaging (MRI), magnetic resonance angiography (MRA), orcomputed tomographic angiography (CTA).

FIG. 1 illustrates a system 100 for designing and generating apatient-specific SDVG. The system 100 includes a printing subsystem 102,a controller 104, and a bioreactor 106. The printing subsystem 102includes a plurality of vats 110(a)-110(c) (collectively referred to asvats 110). The vats 110 are positioned atop a translating stage 112. Alight source 114, mirror 116, and lens 118 are positioned below thetranslating stage 112. The printing subsystem 102 also includes a buildplatform 120. A patient-specific SDVG 122 is coupled to the buildplatform 120. The controller 104 includes an anatomical engine 124 and aCFD engine 126.

The system includes a printing subsystem 102 that is configured tomanufacture the patient-specific SDVG 122 after the patient-specificSDVG 122 is designed by the controller 104. In some implementations, theprinting subsystem 102 can be any 3D printing system. In otherimplementations, the printing subsystem 102 can be a multi-materialbioprinting stereolithography system that can fabricate complexcomposite cellular seeded hydrogels. As an overview, the printingsubsystem 102 can include a plurality of vats that each includesdifferent bioinks. The bioinks can include different resin compositions.The compositions can include different concentrations and species ofmonomer, crosslinkers, cells, and other ingredients. The differentbioinks can be used to form different layers of the printed biomaterial.As described below, the bioprinter can enable high resolution control ofthe printed biomaterial. The high resolution of the bioprinter enablesthe development of a local cellular environment that both chemically andphysically provides the correct stimuli for proliferation,differentiation, and migration of seeded cells. In some implementations,multiple cell types can be seeded in adjacent layers. The systemdescribed herein enables the fabrication of complex hierarchies ofdifferent hydrogels that provide an accurate synthetic facsimile ofintricate biological tissues.

The printing subsystem 102 can solidify distinct bioinks into 3Dstructures using visible light at a resolution of about 30 μm. Theprinting subsystem 102 can build the tissue in a layer-by-layer fashion,in which each layer can include three (or more) distinct custom-madebioinks, with patterned voids that can form the lumen of thepatient-specific SDVG 122.

The printing subsystem 102 includes the translating stage 112. Thetranslating stage 112 is configured to translate across a horizontalplane. In some implementations, the translating stage 112 can translatein a single direction along the horizontal plane, and, in otherimplementations, the translating stage 112 can translate along multipledirections of the horizontal plane. The translating stage 112 isoptically transparent to the light generated by the light source 114such that light can pass through the translating stage 112 to the vats110.

The printing subsystem 102 also includes vats 110 that are coupled to atop surface of the translating stage 112. Each of the vats 110 hold adifferent fluid used in the bioprinting process. For example, the fluidscan include bioinks and cleaning solutions. The controller 104 isconfigured to position a different one of the vats 110 under the buildplatform 120. The bottom of the vats 110 are optical transparent to thelight generated by the light source 114 such that light passing from thelight source 114 (and through the translating stage 112) can pass to theliquid held in the vat 110. In some implementations, the interior,bottom surface of each of the vats 110 is coated with a Teflon coatingto prevent cured bioink layers from sticking to the vat 110. In someimplementations, the cleaning solution includes a solvent bath that cancontain, for example, isopropyl alcohol. In some implementations, thesystem can also include a mechanical cleaning, such as a rubber wiper,that removes excess bioink.

The bioinks stored in the vats 110 are cured (or otherwise solidified)by light emitted by the light source 114. In some implementations, theprinting subsystem 102 can include between about 1 and about 10, betweenabout 1 and about 5, or between 1 and about 3 vats 110 that each storedifferent types of bioinks. The bioinks can include a photo-curablehydrogel and the primary cell type of the intima (e.g., endothelialcells), media (e.g., smooth-muscle cells), or adventitia (e.g.,fibroblast). In some implementations, the bioinks can includebiocompatible hydrogels that polymerize in visible light. The bioinkscan include a hydrogel recipe that includes photoinitiators,co-initiators, and radical scavengers that prevent undesiredpolymerization beyond the mask.

The printing subsystem 102 also includes a light source 114. Layers ofthe bioinks are iteratively cured by light emitted from the light source114. In some implementations, the light source 114 generates light inthe visible spectrum and in other implementations the light source 114generates ultraviolet light. In some implementations, the light source114 includes a laser or an array of LEDs with galvanometers.

The light source 114 of the printing subsystem 102 projects light (inthe visible or ultraviolet range) onto the mirror 116. In someimplementations, the mirror 116 includes an array of digital mirrordevices (DMD). Each of the DMDs can form a “pixel” of a mask. In theseimplementations, the controller 104 controls the state of each of theDMDs to generate the mask. When a DMD is on, it reflects light form thelight source 114 toward the build platform 120. When a DMD is off, itdoes not reflect light. The controller 104 configures the DMDs such thatthe mask corresponds to the next layer of the bioprinted part 122. Theuse of DMDs enables an entire layer of the bioprinted part 122 to becured at once. In other implementations that do not use DMDs, thegenerated light source (e.g., laser beam) is rastered across the bottomof the vat 110 to cure the bioink. In some implementations, the use ofDMDs makes the printing subsystem 102 less sensitive to alignment issuesexperienced by nozzle-based extrusion printers. The printing subsystem102 also includes a lens 118 that can focus the projected light onto thebottom of the translating stage 112 underneath the build platform 120.

In some implementations, the system 100 can include a bioreactor 106that can include a pulsatile flow pump, tubing, acrylic case, pressuresensors, and flow meters. The bioreactor 106 can recirculate mediathrough the patient-specific SDVG 122. Using pressure sensors, thepulsatile flow can be tracked.

In some implementations, applying physiological pulsatile flow canpromote proper alignment and maturation of the cells and extra-cellularmatrix (ECM) in the artery. The artery can be conditioned for varioustime points (e.g., weekly for up to two months) and evaluated for itscellular viability and burst pressure.

In some implementations, the bioprinted artery can be responsive to flowvia vasoconstriction/dilation. Using the bioreactor, varioussteady-state flow rates can be applied while images are continuouslycaptured by a camera. The response to various frequency and amplitudesof pulsatile flow will be measured to characterize the dynamics of thesmooth muscle cells.

The system 100 also includes the controller 104. In someimplementations, the controller 104 can be implemented with a generalpurpose processor, microcontroller, a field-programmable gate array(FPGA), or an application specific integrated circuit (ASIC). In someimplementations, the controller 104 can control the positioning of thevats 110 in relation to the build platform 120. The controller 104 canalso control the intensity and duration of the light emitted from thelight source 114. The controller 104 can also control the generation ofa mask with the mirror 116.

The controller 104 includes the anatomical engine 124 and the CFD engine126. In some embodiments, the CFD engine 126 and the anatomical engine124 can determine factors for the patient-specific SDVG 122 such as, butnot limited to, flow, velocity, pressure, energy loss, shear stress, andforce that the patient-specific SDVG 122 should withstand. Thecontroller 104 can also generate the 3D structure of patient-specificSDVG 122. In some embodiments, when printed, the patient-specific SDVG122 can substantially replicate in vitro graft hemodynamics and wallmechanics and other factors as determined by the CFD engine 126 andanatomical engine 124. In some embodiments, the printed patient-specificSDVG 122 can maintain sufficient blood flow, reduce thrombosis, andreduce inflammation after implantation.

By way of example, but not by way of limitation, consider two patientsbeing considered for CABG with marked clinical heterogeneity. As seen inTable 1, patient-specific differences may appreciably affect SDVGpatency for a multitude of reasons, including graft size, location,angle and path, microcirculatory resistance, flow and perfusionpressure, and revascularization strategy. Other factors such ascompetitive and collateral flow are also vital.

TABLE 1 Patient-Specific Coronary Aortic Known CAD extent Ischemia/Ejection Clinical Characteristics Age Hypertension Diabetes BMItortuosity tortuosity MI and severity Fibrosis Fraction 75 HypertensiveDiabetic 32 Tortuous Tortuous Prior MI LAD, +/+ 15% LCx 60 NormotensiveNon- 24 Non- Non- No prior LAD, +/− 75% diabetic tortuous tortuous MIPDA, PL Anatomic and Micro- Micro- Micro- Graft Graft Graft Micro-Revasculari- Flow/ Flow/ Physiologic Factors circulatory circulatorycirculatory size location/ location/ circulatory zation perfusionperfusion Influencing CABG Patency resistance resistance resistanceangle/ angle/ resistance strategy, pressure pressure Perfusion path pathflow pressure BMI = body mass index; MI = myocardial infarction, CAD =coronary artery disease, LAD = left anterior descending artery; LCx =left circumflex artery; PDA = posterior descending artery; PL =posterolateral branch artery

In some embodiments, patient-specific hemodynamics are integrated intothe production of patient-specific SDVGs by the CFD engine 126. By wayof example, but not by way of limitation, in some embodiments,patient-specific hemodynamics include, but are not limited to,oscillation, stagnation, energy loss of flow through the graft, wallmechanics (e.g., low shear stress and lateral traction on the graftwall), and graft-artery compliance mismatch.

EXAMPLES

The present technology is further illustrated by the following examples,which should not be construed as limiting in any way.

Example 1

CTA of the coronary arteries can be a non-invasive option to theinvasive coronary angiography (ICA). In some implementations, theanatomical engine 124 is configured to receive motion-free images (e.g.,CTA images) at isotropic spatial resolution of about 500 μm. In oneexample, the system 100 performed CTA on 230 patients prior to ICAirrespective of body mass index or heart rate. For stenosis severity,CTA demonstrated a sensitivity, specificity, positive predictive value,and negative predictive value of 94%, 83%, 48%, and 99%, respectively,compared to ICA.

The anatomical engine 124 can receive the CTA data to generate 3Dcoronary artery geometries for image-based modeling of the coronaryanatomy. The anatomical engine 124 determines the artery centerlineextraction 200. Using the center line extraction, the lumen segmentationand stenosis 202 can be performed. The anatomical engine 124 can alsoperform vessel wall segmentation and plaque detection 204 and arteryco-registration 206. The anatomical engine 124 can also performmyocardial segmentation with 3D artery overlay 208. The anatomicalengine 124 can also generate a 17-segment model with 2D flattened arteryoverlay 210.

Example 2

In some implementations, the anatomical engine 124 can generate anatomicinformation about the patient-specific SDVG. The CFD engine 126 cangenerate a hemodynamic profile by performing computational fluiddynamics on the anatomic information. In general, the hemodynamicprofile can include clinical factors, design factors, or other factors,such as flow oscillation, flow stagnation, flow energy loss, wallmechanics, shear levels, and graft-artery compliance mismatch. The CFDengine 126 can calculate the hemodynamic (e.g., physiologic) data of theaortic and coronary artery flow and pressure. FIG. 3 illustrates thepressure, velocity, and flow, as calculated by the CFD engine 126, in apatient-specific SDVG 122. In some implementations, the CFD engine 126can plan “virtual” revascularization strategies by selection of coronaryvessels and locations within vessels to be revascularized for flowfeatures and ischemia reduction. FIG. 4 illustrates a patient-specificSDVG 122 after percutaneous coronary revascularization.

In some implementations, to increase the throughput of patient-specificanalysis, the system can automate parameterized model generation. Usinga patient-specific geometry, a range of perturbed mathematical modelsand simulation meshes are generated according to design parameters ofinterest. Modeling, meshing, submission and retrieval of simulation datafor CFD are automatically run by a script, enabling batch testing of allCABG configurations.

In some implementations, the in vitro flow model can be used tocalculate the wall shear stress (WSS) of the patient-specific SDVG 122(Table 2). WSS has been implicated in the pathogenesis of neo-intimalhyperplasia and thrombus formation.

TABLE 2 In- In- Design CABG Variables silico vitro ObjectiveHemodynamics Flow rate CFD Flow wire; PIV Maximize Pressure gradient CFDPressure wire Minimize Flow separation CFD PIV Minimize Energy loss CFDPressure wire; Minimize PIV Particle residence CFD PIV Minimize timeBlood stress- CFD Rheometer Physiologic strain rate range behavior WallShear stress and CFD PIV physiologic Mechanics traction rangeOscillatory shear CFD PIV Physiologic index range Deformation and FSIPIV High- Physiologic strain speed camera range Strain sensors Internalstress FSI Tensile testing Minimize concentration Strain sensorsStress-strain FSI Tensile testing Physiologic behavior range Long-termDurability — Accelerated Maximize Performance testing Thrombogenicity —Opacity Mass Minimize Biochemistry CFD = Computational Fluid Dynamics;FSI = Fluid Structure Interaction; PIV = Particle Image Velocimetry

In some implementations, the CFD engine 126 can model the cardiovascularphysiology using fluid-structure interactions to quantify thedeformation and stresses on vessel walls. Adoption of fluid-structureinteractions with spatially varying properties better captured the wavepropagation phenomena, which yielded a greater match to in vivo dynamicimaging.

Example 3

Due to the large number of parameters and the complex nature of designobjectives involved, prior CFD designs of CABGs often employed overlysimplistic assumptions—including idealized anatomy, rigid walls andunrealistic boundary conditions—and have approached solutions through“trial-and-error.” In some implementations, the controller 104 can unitecomputer-aided design, CFD, fluid-structure interactions, andoptimization methods to provide a fuller exploration of the permutationsand combinations of physiologically important variables. As an example,the anastomotic angle between the CABG and the coronary artery canaffect hemodynamics considerably. FIG. 4 illustrates marked differencesin velocity and WSS to different angles of distal graft anastomosis insame coronary segment. These computational evaluations can be employedto reduce energy loss and endothelial damage caused by flow impingementsat the native artery, while keeping WSS in the physiologic range andabnormal recirculating flow restricted to a minimal area. The controller104 can use a derivative-free approach to search the large design spaceto identify optimal CABG designs that satisfy the requirement ofminimizing unfavorable flow conditions across a range of physiologicconditions (e.g., rest and exercise); and allows for thorough yetparsimonious selection of integrated designs that can be implemented andtested in vitro.

Example 4

The in vitro measurements were performed in a benchtop flow circulationsystems using patient-specific SDVG physical models fabricated by theprinting subsystem 102 (e.g., a multi-material high-resolution 3Dprinting system). The experiments demonstrated that the system 100 canproduce complex 3D arterial geometries with continuously spatiallyvarying mechanical properties. FIG. 5A illustrates the patient-specificSDVG 122 as a 3D printed physical model. The 3D printed physical modelcan be evaluated for patient-specific, graft-specific and surgicaltechnique-related factors that affect SDVG hemodynamics and wallmechanics by 3 distinct methods: (i) intravascular pressure and flowsensors, (ii) particle image velocimetry (PIV) and (iii) embedded softstrain sensors.

FIGS. 5B and 6 illustrate an in vitro flow circulation system that wasconstructed that approximates coronary flow physiology to determine therelationship between an input defined flow rate (Q), proximal (Pa) anddistal (Pd) pressures, and the resistance of a stenosis (Rp) and distalmicrovasculature (Rd). The flow circulation system can include aprogrammable flow pump, patient-specific vascular models, downstreamresistance modules, and intravascular pressure and flow sensors. In theflow circulation system, a coronary artery was made with a 50% stenosisusing 3D printing, and used the pump to prescribe three flow rates, withvarying microvascular resistance to match four levels of proximalpressures. Distal pressure differed markedly according to perturbationsin flow and resistance, and confirmed the importance of inflow/outflowconditions to optimize within-vessel pressures.

PIV was also integrated into the flow circulation system. FIGS. 5C and5D illustrate the tracking of particle movements and computing ofpulsatile fluid velocities within small-diameter tubular models.Velocity measurements by PIV were in accordance with pressuremeasurements described above, and demonstrate that the integration of invitro flow and PIV systems allows us to not only measure pressure, butalso velocities with high spatial (about 20 μm) and temporal resolution(about 100 Hz).

In some implementations, 3D printed soft strain sensors, generated bydirect nozzle extrusion of conductive hydrogels and dielectricelastomers, are incorporated into the SDVG. The sensor's softness (<1MPa) and small size (<0.5 mm total thickness) allow integration withinSDVG for continuous monitoring of strain profiles. This technique isadvantageous because multiple sensors can be embedded at differentpositions within the SDVG. A sampling of hemodynamics and mechanics thatcan be evaluated by the sensors is listed in Table 2.

Example 5

FIG. 7 illustrates the mechanical properties of a coronary artery. Insome implementations, the system 100 can generate the patient-specificSDVG 122 responsive to the mechanical properties of the coronary arterysuch that the patient-specific SDVG 122 has biomimetic features thatoffer the favorable hemodynamics, biocompatibility, and durability.

Example 6

FIG. 8 illustrates a tiered approach to validate a SDVG for CABG surgeryusing in silico, in vitro, and in vivo methods. The optimized SDVG willbe tested in a flow system with patient-specific anatomy fabricated by3D printing (in vitro) as described herein. Both the in silico and thein vitro work will yield an optimized patient-specific CABG, whoseperformance will be validated in vivo in swine.

Example 7

In some implementations, the anatomical engine 124 can perform cardiacsegmentation in a mathematical model-based method by employing machinelearning to automatically delineate structures in the thorax bycapitalizing on the relative stablity of the spatial and topologicalrelationship between different organs. A medial model, which equallydivides the organ wall into inner and outer layers by implicit thicknessdefinition, can be used as the cardiothoracic shape template. Thesegmentation can be used to detected landmarks for the establishment ofan affine transform, which allows deforming the template to the imagevolume to be segmented. The template surface of each structure is thenrefined so that the template surfaces correctly align with the targetboundaries in the image.

Example 8

In some implementations, given the proximal and distal anastomosis sitesA_(p) and A_(d), the geometry of patient-specific SDVG can beparameterized by coordinates of the control nodes in between c₁, . . . ,c_(l) and diameters, d₁, . . . d_(l). Therefore, the smoothpatient-specific SDVG model can be uniquely defined by a combination ofthese primary parameters. Other common variables such as orifice size atthe anastomosis, graft length, and curvature are derived from theprimary parameters (Table 3). Design of the patient-specific SDVGsurface model can include defining a local coordinate frame [t,u,v] ateach node using a rotation minimization technique, where t is along thetangent direction of the centerline and [u,v] spans a 2D plane on thecross-section. The graft surface is created as a structured mesh bysweeping through the contours on the 2D planes, which is extremely fastand allows for both real-time interactive editing and programmablemodified by an algorithm.

In some implementations, the anatomical engine 124 can employ acollision detection method. The collision detection method can avoid thepath of the graft passing through the segmented critical anatomicstructures (e.g., pulmonary artery) by constraining the search space ofthe parameters. Collision is detected via a two-step acceleratedalgorithm by checking whether the graft crosses the bounding box of thecritical structures, and then verifying that the graft passes throughthe critical structures themselves. The speed of the second step may beimproved by inserting the surface triangles into a tree data structure,e.g., the AABB tree, which decreases the computational complexity fromO(n) to O(lgn). To prevent the complex twisted, elongated and tortuouspathways commonly seen in vein grafts, the anatomical engine 124 can usea method to search for the path of minimal length and curvature,satisfying the following conditions: both ends correspond to proximaland distal anastomosis sites and the path of the graft does not crosscritical structures. Search based dynamic programming can be used byalternating between proposing a simplified path candidate and varyingthe path to avoid collision.

TABLE 3 CABG Design Parameters Design Constraint and Consideration Graftpath Critical cardiothoracic structures Graft length and curvature Graftdiameter Coronary artery diameter Anastomosis orifice size Anastomosissites Aortic and coronary anatomy Location of coronary stenosisAnastomosis geometry Coronary artery diameter Anastomosis angle Ease ofAnastomosis Wall mechanical properties Aorta and coronary arterystiffness Cardiac motion Wall thickness Graft strength Graft weight

Example 9

In some implementations the CFD engine 126 can generate boundaryconditions for the graft wall. For the fluid-structure interactionsmethod, fluid structure interaction based on the ArbitraryLagrangian-Eulerian Method can be used. The methods can couple the wallmechanics and the blood flow together and can be capable of handlingpotential large displacements. In some implementations, externalstructures attached to the vessels and the graft can be modeled usinglumped boundary conditions. To test the efficiency of flow augmentationby the graft and to avoid high power assumption and damage risk, thestiffness and strength of the materials can be another set of parametersused to calculate the wall thickness t and the mechanical stiffness E ofthe patient-specific SDVG (Table 3). Allowing spatial variation of theseparameters provide extra flexibility that the graft should match at bothanastomoses to the differing stiffness of the aorta and coronaryarteries.

Example 10

In some implementations, the CFD engine 126 can determine the influenceof the local hemodynamics in the patient-specific SDVG and how thepatient-specific SDVG changes the downstream blood flow patterns. TheCFD engine 126 can improve the patient-specific SDVG design using CFD,objective functions, and hemodynamic variables (Table 2) of interest,e.g., flow rate, pressure gradient, shear stress and traction on thewall, flow oscillation and separation, energy loss, graft motion anddeformation, or a combination of several variables.

In some implementations, the hemodynamic objective functions can becombined with shape-based features, such as curvature or length to avoidoverly complex geometry. A general patient-specific SDVG optimizationproblem can be written as min F(P), s.t. G_(min)≤G(P)≤G_(max), where Pis a vector of all parameters in patient-specific SDVG design, and F(P)is the target function to be optimized, e.g., maximizing the flow ratein the patient-specific SDVG. G(P) represents the list of constraints inthe hemodynamic targets that fall between the preferred ranges G_(min)and G_(max). First, the most sensitive parameters will be chosen tooptimize for the variable of interest. For example, wall stiffness maybe omitted when WSS is considered; WSS varies only 5% with differentlevels of stiffness in physiologic ranges.

Example 11

In some implementations, the 124 can use both idealized andpatient-specific geometries from a group of patients to generate thepatient-specific SDVG. The idealized geometry will include cylindricalshapes of ascending aorta and several coronary arteries, which is usefulto generate plausible data before adding geometric complexity,especially when simulating for novel strategies. For patient-specificevaluation, common geometries and potential failure modes will beidentified. To address the possibility that an overall optimal designmay be achieved by a combination of parameters when each parameter issuboptimal, an iterative approach by repeating the optimization underthe updated parameters will be used. Qualitative and quantitativeanalysis will be performed to determine conceptual soundness,generalizability and statistical significance of such findings.

Example 12

In some implementations, the CFD engine 126 calculates hemodynamics andwall mechanics variables using: (i) pressure measurements received frompressure gauges, (ii) pressure and flow measurements received fromguidewires, (iii) flow velocities received response to PIV, particleresidence time, and oscillation, and (iv) strain measurements.

Example 13

In some implementations, the system can evaluate the effects of anygiven variable on hemodynamics by fixing the geometry of the aorta andcoronary arteries. This is done to identify an optimal patient-specificSDVG geometry without the need to initially consider wall deformation.Single geometric variables that have been suggested by the CFD engine126 and fluid-structure interactions are explored one-by-one, andsystematically varied to determine their weighted effects.

Single variables that can improve hemodynamics are assessed incombination with each other to determine their additive or synergisticimprovement effects, as well as fluid-structure interactions-predictedcontributions by using elastic walls. Patient-specific SDVGs can beproduced in a step-wise fashion to determine the salutary effects of anyindividual variable on a multivariable integrated SDVG.

Upon identifying the optimal SDVG design over this wide parameter space,patient-specific models based upon CTA are evaluated by the CFD engine126. Cardiothoracic anatomy and native coronary artery mechanicalproperty information are included in the evaluation. The models aretested on different patient types. Patients that differ in the followinganatomic findings are chosen: (i) cardiothoracic size and geometry; (ii)aortic size, tortuosity and location, (iii) coronary artery size,tortuosity and location, and (iv) coronary artery disease extent andseverity.

Example 14

The benefits of the optimized SDVG may be mitigated when implanted invivo due to the complexity and variability in surgical techniques. Toaccount for this, “benchtop surgery” in vitro to assess the sensitivityneeded to realize beneficial SDVG hemodynamics is performed. Forexample, if significant variability is present from SDVG angles,orientation markers to the surgeon for ideal SDVG angle deployment areprovided. Likewise, SDVGs with orifices that cannot be sutured to lessthan a certain size can be produced. In some embodiments, “benchtopsurgery” physical models included within-subject controls usingpolyurethane and, if additive, polytetrafluoroethylene (PTFE), expandedpolytetrafluoroethylene (ePTFE), and polyethylene terephthalate (PET).

Example 15

To ensure the optimized SDVG has sufficient durability to withstand thelong-term cyclical strains in vivo, accelerated cyclic durability testswill be performed. These tests will include of applying strains,slightly higher than physiological levels (˜40% radial strain) at afrequency 5 times higher than that of the normal heart rate. Testingwill be conducted for 18 days, and will therefore assess the durabilityof the optimized SDVG equivalent to normal conditions for 90 days.

Example 16

Reductions in thrombogenicity may occur through optimizing SDVGhemodynamics and wall mechanics. The thromboresistive nature offabricated SDVGs will be assayed by the following methods: (i) opticalmethods that identify changes in opacity in transparent SDVGs tocharacterize time, size and distribution of thrombi formation; (ii) massmeasurements using change in weight of SDVG before and after bloodperfusion; and (iii) platelet activity using low shear impedanceaggregometry and hematologic parameters. Non-treated blood, bloodtreated with aspirin (81 mg equivalent), and aspirin plus clopidogrel(75 mg equivalent) will be tested. Aspirin and aspirin plus clopidogreltreatments will be considered standard-of-care for individualspost-CABG. Optimized CABGs will be compared to other SDVG, includingpolyurethane, ePTFE, and polyethylene terephthalate. OCT will be used toevaluate both durability and thrombogenicity. For durability, surfacedetails of the SDVG will be examined and thrombosis will be monitoredafter whole blood circulation for fabricated SDVGs that are nottransparent.

Example 17

The efficacy and safety of optimized SDVGs in vivo in naïve domesticswine will be assayed, with a primary endpoint of the 90-day patencyrates of optimized CABG SDVGs compared to conventional polyurethane SDVGcontrols. Patency will be assessed by gross inspection, intravascularimaging and pathology. Other factors that will be monitored include: (i)safety, (ii) assessment of native coronary vessels for thrombosis orocclusion, and (iii) assessment of device performance and handling.Safety will be evaluated through gross and histological analysis of (a)morphometry parameters (internal elastic membrane (IEM)-based %stenosis, neointimal thickness and area, medial area and lumen area, IELand external elastic membrane area); (b) morphology parameters(including scoring for inflammation, thrombus, endothelialization,medial smooth muscle cell proliferation or loss, fibrin deposition,injury and fibrosis); (c) scanning electron microscopy of a subset ofvessels for endothelialization and micro-thrombus formation; and (d)documentation of device-related adverse events.

FIG. 9 illustrates the timeline for swine undergoing coronary arterybypass surgery. Surgery will be performed at the CRF Skirball Center forIntervention. All grafts will be anastomosed in end-to-side fashion. Allsubjects will receive aspirin preoperatively, with clopidogrel initiatedthe day after surgery, in accordance with treatment for patientsundergoing CABG. Days-to-euthanasia will be 7, 30 and 90, withechocardiography, intravascular imaging (by angiography, OCT andintravascular ultrasound [IVUS]) performed before termination.Non-terminal echocardiography at 7, 30, and 90 days for animals thathave not been euthanized will be performed.

Thirty-six naïve domestic swine will undergo surgery with appropriatewithin-subject and between-subject controls (FIG. 10). CABG surgicalmodels account for the number of bypassed vessels (⅓ vs. 3/3), the %stenosis in the native coronary vessels (70% vs. 100%) and the specificvessels being bypassed (left anterior descending, left circumflex orright coronary arteries) with a conventional polyurethane graft as acomparator in each case (Table 4). To maximize the number of optimizedCABGs for evaluation, surgeries will be performed in a 2:1 ratio (e.g.,optimized CABG: polyurethane control) for all between-subjectcomparisons.

TABLE 4 # % Stenosis Bypassed Native N Comparator Vessels Vessel(s) LADLCx RCA (=36) Between-subject 1  70% oCABG None None 6 Between-subject 1 70% PU None None 3 Between-subject 1 100% OCABG None None 6Between-subject 1 100% PU None None 3 Within-subject 3  70% oCABG oCABGPU 3 Within-subject 3  70% oCABG PU oCABG 3 Within-subject 3  70% PUoCABG oCABG 3 Within-subject 3 100% oCABG oCABG PU 3 Within-subject 3100% oCABG PU oCABG 3 Within-subject 3 100% PU oCABG oCABG 3 LAD = Leftanterior descending artery; LCx = Left circumflex artery; RCA = Rightcoronary artery; oCABG = optimized CABG; Pu = polyurethane

Swine will be analyzed by (a) laboratory analysis, (b) echocardiography,(c) intravascular imaging (ICA, OCT and IVUS), and (d) pathology.Intravascular imaging and pathology will be performed only at theterminal date prior to euthanasia. Blinded expert core laboratoryreaders will perform all imaging (Cardiovascular Research Foundation orDalio Institute of Cardiovascular Imaging, New York, N.Y.) and pathologystudies (CV Path, Gaithersburg, Md.).

Laboratory Analysis. Blood samples will be evaluated following overnightfast for hematology, chemistry, and fibrinogen.

Echocardiography. Transthoracic echocardiography will be performedemphasizing surrogate markers of graft closure (e.g., ejection fraction,ventricular wall motion abnormalities, and diastolic function).

Intravascular Imaging. Intravascular imaging will be performed attermination. ICA will be performed in accordance with societalguidelines to assess SDVG patency. ICAs will be evaluated byquantitative coronary angiographic (QCA) for stenosis severity. OCT—withits high spatial (10 μm) resolution—will be used to evaluate twocomplications of the optimized CABG: thrombogenicity and structuraldefects, but can also assess atheroma, macrophage accumulation, andintimal hyperplasia. IVUS will be used for tissue characterization ofstenosis and intimal hyperplasia; as well as for external and internalelastic lamina area, lumen area; and derived values of neointimal areaand thickness, medial area, and lumen diameter.

Pathology will be performed post-mortem. Vessels will be perfusionfixed, with test arteries and SDVGs excised as a whole. Treated vesselswill be examined by a pathologist for vessel laceration, hematoma, orthrombus associated with the treatment and/or delivery system. Allsamples will be embedded in paraffin, sectioned and stained, thensubjected to analysis for biological responses, including inflammation,thrombus, endothelialization, intimal hyperplasia, fibrin deposition,injury, hemorrhage, and necrosis.

FIG. 10 illustrates an example method 1000 for producing apatient-specific vascular graft. The method 1000 includes receiving atleast one CTA image of a subject (step 1002). The method 1000 includesdetermining a coronary artery volumetric geometry responsive to the CTAimage (step 1004). The method 1000 also includes generating ahemodynamic profile (step 1006). The method 1000 also includes producinga patient-specific vascular graft (step 1008).

As set forth above, the method 1000 includes receiving CTA image data(step 1002). In some implementations, the CTA image data includes imagedata of a patient's heart or vascular system. The image data can includeimage data of small diameter arteries and veins. In someimplementations, the patient is given an iodine-rich contrast materialprior to imaging by a computer tomography device. It should beunderstood that, in some implementations, the method 1000 can instead becarried out using a different type of medical image. For example,instead of receiving a CTA image, the method 1000 can include receivingan MRI or MRA image.

The method 1000 can also include determining a coronary arteryvolumetric geometry (step 1004). In some implementations, determiningthe coronary artery volumetric geometry can include determining anartery centerline of the patient's coronary artery (or other artery).The method 1000 can also include segmenting the coronary arteryvolumetric geometry.

The method 1000 can also include generating a hemodynamic profile (step1006). In some implementations, the hemodynamic profile can includeclinical factors (e.g., any of the factors shown in Table 2) and designfactors (e.g., any of the factors shown in Table 3), which may includeflow oscillation, flow stagnation, flow energy loss, wall mechanics,shear levels, and graft-artery compliance mismatch.

The method 1000 can also include producing the patient-specific vasculargraft (step 1008). As described above, the graft can be produced with a3D printer. In some implementations, the graft can have an internaldiameter between about 0.5 mm and about 6 mm. In some implementations,the 3D printer is a multi-material bioprinting stereolithography systemthat can fabricate complex composite cellular seeded hydrogels. In someimplementations, the 3D printer can print the patient-specific vasculargraft using at least one biocompatible polymer. For example,biocompatible polymers may include urethanes, silicone, PET, PTFE, andePTFE. The 3D printer also can be configured to print thepatient-specific vascular graft such that the patient-specific vasculargraft exhibits continuously spatially varying mechanical propertiesalong at least a portion of its length. In some implementations, thiscan be achieved by varying the materials (or combinations of materials)used to form the patient-specific vascular graft or by varying a wallthickness of the patient specific vascular graft along the length of thegraft.

In some implementations, determining the coronary artery volumetricgeometry (step 1004) can include determining an artery centerline of thecoronary artery. The method 1000 also can include forming lumensegmentation of the coronary artery volumetric geometry, as discussedabove.

In some implementations, the method 1000 also includes calculating atleast one of a flow, a velocity, a pressure, or a shear stress of thepatient-specific vascular graft. For example, the patient-specificvascular graft can be incorporated into a flow system having a pump andat least one measurement tool, such as a pressure gauge, a flow gauge, avelocity gauge, a particle image velocimetry device, a pressure guidewire, a flow guide wire, an optical coherence tomography device, or astrain sensor. In some implementations, measuring the flowcharacteristics and performances in the 3D printed patient-specificvascular graft can better approximate the flow characteristics andperformances of the patient's anatomy when compared to computationalmodels of the flow characteristics and performances. An example of sucha flow system is described below in connection with FIG. 11. In someimplementations, generating the hemodynamic profile (step 1006) can beachieved by applying an optimization technique based on a set ofparameters including at least one of a flow rate, a pressure gradient, ashear stress, or a flow oscillation. The choice may closely depend onthe design target (e.g. maximizing flow or increasing shear stress) andmay sometimes lead to compromise. For example, increasing the graftdiameter may augment the flow rate, but shear stress may be decreased toabnormal ranges as a result. In addition, experimental studies haveshown certain variables are considered normal when falling in a saferange. Further, hemodynamic objective functions can be combined withshape-based features of the patient-specific vascular graft, such ascurvature or length, to avoid overly complex geometry. A generaloptimization problem can be written as min F(P), s.t.G_(min)≤G(P)≤G_(max), where P is a vector of all parameters in graftdesign, and F(P) is the target function to be optimized, e.g. maximizingthe flow rate in the graft. G(P) represents the list of constraints inthe hemodynamic targets that fall between the preferred ranges G_(min)and G_(max). While the optimization is difficult to solve because ofunavailable gradient and expensive functional evaluation of F(P) andG(P), the problem may be simplified by limiting the number of variablesand parameters involved, automating the procedures of shape design andexecution, and taking advantage of derivative-free algorithms. In someimplementations, the most sensitive parameters can be chosen to optimizefor the variable of interest. For instance, wall stiffness may beomitted when WSS is considered WSS varies only 5% with different levelsof stiffness in physiologic ranges. In some implementations, aderivative-free method, e.g. Nelder-Mead method or genetic algorithmsmay be used to perform optimization tasks.

FIG. 11 illustrates an example flow system 1100 with a patient-specificvascular graft. The system 1100 includes the patient-specific vasculargraft 1103 (labeled in FIG. 11 as an aorta mimic), a stenosed coronaryartery 1107 coupled to a fill port 1109. The patient-specific componentsof the system 1100 can be generated with the 3D printing methodsdescribed herein. The system 1100 also includes a peristaltic pump 1105,pressure sensors 1110 a and 1110 b (generally referred to as pressuresensors 1110), a data-acquisition board (DAQ) 1115, a computing device1120, and PIV device 1125. The system 1100 can mimic idealized orpatient-specific physical models of the aorta, coronary arteries andoptimized CABG, including single or diffuse stenosis, and coronarycollateral circulations that contribute to competing flow.Coronary-specific in vitro boundary conditions can be developed togenerate realistic coronary pressure waveforms by combining resistorsand capacitors, such as the variable resistor 1130. Further, calibrationof the system 1100 can be performed against a unique multicenterdatabase, which may include more than 250 patient-specific CTAs andlocation-specific invasive gold standard pressure measurements. Thesystem 1100 can accommodate a variety of measurement tools, includingpressure, flow and velocity gauges (such as the pressure sensors 1110);the PIV device 1125; pressure and flow guide-wires; an optical coherencetomography (OCT) device; and integrated strain sensors. The computingdevice 1120 can receive information from any of these measurement tools,and can process the information to evaluate the physical modelrepresented by the system 1100. Thus, the system 1100 can enable“benchtop surgery,” allowing optimized CABGs to be physically sutured tothe physical models.

An embodiment of the disclosure relates to a non-transitorycomputer-readable storage medium having computer code thereon forperforming various computer-implemented operations. The term“computer-readable storage medium” is used herein to include any mediumthat is capable of storing or encoding a sequence of instructions orcomputer codes for performing the operations, methodologies, andtechniques described herein. The media and computer code may be thosespecially designed and constructed for the purposes of the embodimentsof the disclosure, or they may be of the kind well known and availableto those having skill in the computer software arts. Examples ofcomputer-readable storage media include, but are not limited to:magnetic media such as hard disks, floppy disks, and magnetic tape;optical media such as CD-ROMs and holographic devices; magneto-opticalmedia such as optical disks; and hardware devices that are speciallyconfigured to store and execute program code, such asapplication-specific integrated circuits (ASICs), programmable logicdevices (PLDs), and ROM and RAM devices.

Examples of computer code include machine code, such as produced by acompiler, and files containing higher-level code that are executed by acomputer using an interpreter or a compiler. For example, an embodimentof the disclosure may be implemented using Java, C++, or otherobject-oriented programming language and development tools. Additionalexamples of computer code include encrypted code and compressed code.Moreover, an embodiment of the disclosure may be downloaded as acomputer program product, which may be transferred from a remotecomputer (e.g., a server computer) to a requesting computer (e.g., aclient computer or a different server computer) via a transmissionchannel. Another embodiment of the disclosure may be implemented inhardwired circuitry in place of, or in combination with,machine-executable software instructions.

As used herein, the singular terms “a,” “an,” and “the” may includeplural referents unless the context clearly dictates otherwise.

As used herein, relative terms, such as “above,” “below,” “up,” “left,”“right,” “down,” “top,” “bottom,” “vertical,” “horizontal,” “side,”“higher,” “lower,” “upper,” “over,” “under,” “inner,” “interior,”“outer,” “exterior,” “front,” “back,” “upwardly,” “lower,” “downwardly,”“vertical,” “vertically,” “lateral,” “laterally” and the like refer toan orientation of a set of components with respect to one another; thisorientation is in accordance with the drawings, but is not requiredduring manufacturing or use.

As used herein, the terms “connect,” “connected,” and “connection” referto an operational coupling or linking. Connected components can bedirectly or indirectly coupled to one another, for example, throughanother set of components.

As used herein, the terms “approximately,” “substantially,”“substantial” and “about” are used to describe and account for smallvariations. When used in conjunction with an event or circumstance, theterms can refer to instances in which the event or circumstance occursprecisely as well as instances in which the event or circumstance occursto a close approximation. For example, when used in conjunction with anumerical value, the terms can refer to a range of variation less thanor equal to ±10% of that numerical value, such as less than or equal to±5%, less than or equal to ±4%, less than or equal to ±3%, less than orequal to ±2%, less than or equal to ±1%, less than or equal to ±0.5%,less than or equal to ±0.1%, or less than or equal to ±0.05%. Forexample, two numerical values can be deemed to be “substantially” thesame if a difference between the values is less than or equal to +10% ofan average of the values, such as less than or equal to ±5%, less thanor equal to +4%, less than or equal to ±3%, less than or equal to ±2%,less than or equal to +1%, less than or equal to ±0.5%, less than orequal to ±0.1%, or less than or equal to ±0.05%.

Additionally, amounts, ratios, and other numerical values are sometimespresented herein in a range format. It is to be understood that suchrange format is used for convenience and brevity and should beunderstood flexibly to include numerical values explicitly specified aslimits of a range, but also to include all individual numerical valuesor sub-ranges encompassed within that range as if each numerical valueand sub-range is explicitly specified.

While the present disclosure has been described and illustrated withreference to specific embodiments thereof, these descriptions andillustrations do not limit the present disclosure. It should beunderstood by those skilled in the art that various changes may be madeand equivalents may be substituted without departing from the truespirit and scope of the present disclosure as defined by the appendedclaims. The illustrations may not be necessarily drawn to scale. Theremay be distinctions between the artistic renditions in the presentdisclosure and the actual apparatus due to manufacturing processes andtolerances. There may be other embodiments of the present disclosurewhich are not specifically illustrated. The specification and drawingsare to be regarded as illustrative rather than restrictive.Modifications may be made to adapt a particular situation, material,composition of matter, technique, or process to the objective, spiritand scope of the present disclosure. All such modifications are intendedto be within the scope of the claims appended hereto. While thetechniques disclosed herein have been described with reference toparticular operations performed in a particular order, it will beunderstood that these operations may be combined, sub-divided, orre-ordered to form an equivalent technique without departing from theteachings of the present disclosure. Accordingly, unless specificallyindicated herein, the order and grouping of the operations are notlimitations of the present disclosure.

1-22. (canceled)
 23. A method comprising: receiving at least one medicalimage of a subject; determining a coronary artery volumetric geometry ofthe subject responsive to receiving the at least one medical image;determining, based on the coronary artery volumetric geometry, asubject-specific hemodynamic profile; generating a subject-specificvascular graft model using the determined coronary artery volumetricgeometry and the hemodynamic profile; and producing a subject-specificvascular graft based on the subject-specific vascular graft model. 24.The method of claim 23, wherein the subject-specific hemodynamic profilequantifies, based on subject-specific fluid-structure interactions,deformation and stress on vessel walls of the subject.
 25. The method ofclaim 24, wherein generating the subject-specific vascular graft modelcomprises defining one or more subject-specific design parameters basedon the deformation and stress on the vessel walls of the subject. 26.The method of claim 23, wherein producing the subject-specific vasculargraft comprises printing the subject-specific vascular graft with a 3Dprinter.
 27. The method of claim 26, wherein the subject-specificvascular graft is formed from a biocompatible polymer.
 28. The method ofclaim 26, wherein the subject-specific vascular graft exhibitscontinuously spatially varying mechanical properties along at least aportion of its length.
 29. The method of claim 23, wherein thesubject-specific vascular graft has an internal diameter between about0.5 mm and about 6 mm.
 30. The method of claim 23, wherein determiningthe coronary artery volumetric geometry comprises determining an arterycenterline of the coronary artery.
 31. The method of claim 23, furthercomprising performing lumen segmentation of the coronary arteryvolumetric geometry.
 32. The method of claim 23, wherein determining thehemodynamic profile comprises applying computational fluid dynamics(CFD) to determine at least one of a flow, a velocity, a pressure, or ashear stress.
 33. The method of claim 32, further comprising calculatingat least one of the flow, the velocity, the pressure, or the shearstress of the subject-specific vascular graft using a flow systemcomprising a pump and at least one measurement tool.
 34. The method ofclaim 33, wherein the at least one measurement tool comprises at leastone of a pressure gauge, a flow gauge, a velocity gauge, a particleimage velocimetry device, a pressure guide wire, a flow guide wire, anoptical coherence tomography device, or a strain sensor.
 35. The methodof claim 23, wherein determining the subject-specific hemodynamicprofile further comprises applying an optimization technique based on aset of parameters including at least one of a flow rate, a pressuregradient, a shear stress, or a flow oscillation.
 36. The method of claim23, wherein the at least one medical image comprises at least onecomputed tomographic angiography image.
 37. A system comprising at leastone processor and a memory unit storing processor executableinstructions, wherein execution of the processor executable instructionsby the at least one processor causes the at least one processor to:receive at least one medical image of a subject; determine a coronaryartery volumetric geometry of the subject responsive to receiving the atleast one medical image; determine, based on the coronary arteryvolumetric geometry, a subject-specific hemodynamic profile; generate asubject-specific vascular graft model based on the coronary arteryvolumetric geometry and the hemodynamic profile; and generateinstructions for producing a subject-specific vascular graft based onthe subject-specific vascular graft model.
 38. The system of claim 37,wherein execution of the processor executable instructions furthercauses the at least one processor to determine the subject-specifichemodynamic profile by quantifying, based on subject-specificfluid-structure interactions, deformation and stress on vessel walls ofthe subject.
 39. The system of claim 38, wherein execution of theprocessor executable instructions further causes the at least oneprocessor to generate the subject-specific vascular graft model bydefining one or more subject-specific design parameters based on thedeformation and stress on the vessel walls of the subject.
 40. Thesystem of claim 37, wherein the instructions for producing thesubject-specific vascular graft comprise instructions for 3D printingthe patient-specific vascular graft.
 41. The system of claim 37, whereinexecution of the processor executable instructions further causes the atleast one processor to determine the coronary artery volumetric geometryby determining an artery centerline of the subject.
 42. The system ofclaim 37, wherein execution of the processor executable instructionsfurther causes the at least one processor to calculate at least one of aflow, a velocity, a pressure, or a shear stress based on an outputreceived from at least one measurement tool included within a flowsystem that also comprises a pump, wherein the at least one measurementtool comprises at least one of a pressure gauge, a flow gauge, avelocity gauge, a particle image velocimetry device, a pressure guidewire, a flow guide wire, an optical coherence tomography device, or astrain sensor.